电成像测井新参数在碳酸盐岩岩相识别中的应用

电成像测井识别碳酸盐岩岩相主要有定性图版法、定量参数法和机器学习法。图版法简单实用,但其效率低且受人工经验影响大,图像纹理参数种类过多且使用复杂,机器学习法需要大量样本标签,应用受到局限。为此,提出了一种简单又高效的新定量参数法,即通过新定量参数判别岩石构造特征来识别不同岩相。首先采用地质统计法对电成像测井动态图像进行全井眼插值,然后针对全井壁覆盖图像,利用数字图像处理技术获取二值图像,最后分别统计二值图像在纵向和横向黑色斑点(块)最大个数,并将二者的比值定义为视岩石构造数ARSN(aparent rock structure number)。ARSN可以有效区分块状和薄层状构造。据野外露头及岩心观察,一般颗粒云岩相为块状或厚层状和泥晶云岩相为薄层状或薄互层状构造特征。因此,利用ARSN可以区分颗粒云岩和泥晶云岩两大类岩相。以四川盆地M地区龙王庙组为例,经取心井验证,岩相识别符合率80%以上。该方法效率高且不受人的因素影响,实现高精度岩相识别,为该区碳酸盐岩沉积微相精细研究提供了有力技术支撑。

Application of new quantitative parameters of electrical imaging logging in carbonate facies identification

At present, the qualitative chart method is widely used for the identification of carbonate lithofacies by electrical imaging logging, which lacks effective quantitative identification parameters. Although the chart method can identify lithofacies, it is affected by the experience and has low efficiency, which leads to great difference of lithofacies identification. Therefore, a new quantitative parameter of electrical imaging logging is proposed to distinguish different lithofacies by identifying structural characteristics of rocks. Firstly, the whole borehole image is obtained by interpolation of the dynamic image of electrical imaging logging, and then the binary image is obtained by digital image processing technology. Finally, the maximum number of vertical and horizontal black spots (blocks) in the binary image is counted respectively, and the ratio of them is defined as new parameter (ARSN). ARSN can be effectively divided into massive (thick layer) structure and thin layer (thin interbedded) structure. Generally, grain facies is massive (thick layer) structure, and micrite facies is thin layer (thin interbedded) structure. Therefore, ARSN can be used to distinguish two types of lithofacies: grain rock and micrite rock. Taking Longwangmiao formation in M area of Sichuan Basin as an example, according to the verification of core wells, the coincidence rate of lithofacies identification is more than 80%. This method improves the efficiency of lithofacies identification of electrical imaging logging, and is not affected by human factors. It realizes stable and high-precision lithofacies identification, which provides strong technical support for the study of sedimentary microfacies in this area.